The Challenge
A mid-size rare disease biotech was running 3 critical Phase II/III trials but had no integrated RBQM solution. Their data lived in silos across EDC, ePRO, Safety Database, and IRT systems. The clinical operations team was manually pulling data from each system into Excel spreadsheets every week, a process that took 12+ hours and was prone to errors.
Pain Points
- • Data scattered across 5 different systems
- • 12+ hours/week spent on manual data aggregation
- • Risk signals identified 2-3 weeks too late
- • No visibility into cross-system data quality issues
- • Executive team had no portfolio-level risk view
Business Requirements
- • Integrate data from EDC, ePRO, Safety DB, IRT
- • Real-time risk dashboards for CRAs and monitors
- • Automated data quality reconciliation
- • Portfolio-level executive dashboard
- • Budget constraint: $150K total project cost
Why Not a Commercial RBQM Platform?
The client evaluated commercial RBQM platforms but faced two major obstacles:
- 1.Cost: Commercial platforms quoted $200K-$400K for 3 trials (above budget)
- 2.Integration complexity: Their ePRO vendor used a proprietary API that commercial platforms didn't support natively, requiring custom integration work that would add 3-4 months and $100K+ to the timeline
A custom solution built specifically for their ecosystem was the most cost-effective and fastest path forward.
The Solution
I designed and built a custom RBQM analytics platform using R Shiny, integrating data from all 5 source systems into a unified risk management dashboard. The platform was delivered in 6 months for $135K (under budget).
Phase 1: Architecture & Data Integration (Months 1-2)
Built the data pipeline to pull and harmonize data from all source systems.
- API Integration: Built custom R scripts to pull data from EDC (Oracle), ePRO (proprietary API), Safety Database (SQL), and IRT (REST API)
- Data Warehouse: Set up PostgreSQL database to store harmonized data with automated daily refresh
- Data Quality Checks: Implemented automated reconciliation scripts to flag discrepancies between systems (e.g., patient enrolled in EDC but not in IRT)
Phase 2: Analytics & Dashboards (Months 3-4)
Developed the R Shiny application with role-based dashboards.
- CRA Dashboard: Site-level risk view with KRIs for enrollment, data quality, protocol deviations, and AE reporting
- Data Manager Dashboard: Cross-system data quality metrics with automated reconciliation reports
- Executive Dashboard: Portfolio-level risk heatmap showing status across all 3 trials with drill-down capability
- Automated Alerts: Email notifications when KRI thresholds are breached or data quality issues detected
Phase 3: Validation & Deployment (Months 5-6)
Validated the platform and trained the team for production use.
- Risk-Based Validation: Used CSA approach to validate high-risk calculations and data integrations (150-page validation report vs. 1,200+ pages with traditional CSV)
- User Training: Conducted 6 training sessions for CRAs, data managers, and executives with hands-on exercises
- Documentation: Created user manuals, admin guides, and code documentation for future maintenance
- Production Deployment: Hosted on AWS with automated backups and monitoring
Technical Architecture
Technology Stack
- Frontend:R Shiny with custom CSS/JavaScript
- Backend:R (data processing), PostgreSQL (data warehouse)
- Integrations:REST APIs, SQL connectors, custom R packages
- Hosting:AWS EC2 with RStudio Connect
- Security:LDAP authentication, role-based access control
Key Features
- Real-time data refresh (daily automated updates)
- 35+ KRIs across enrollment, data quality, safety
- Automated data reconciliation between systems
- Email alerts for threshold breaches
- Exportable reports (PDF, Excel)
- Mobile-responsive design for tablet access
Transformation at a Glance
Before Platform
Manual processes, disconnected systems, reactive monitoring
After Implementation
Integrated platform with automated workflows and proactive risk detection
Before Platform
After Implementation
The Results
Additional Outcomes
Operational Impact
- Identified 2 sites with systematic data entry errors within first month
- Detected EDC-ePRO data discrepancies affecting 8% of patients
- Executive team gained real-time portfolio visibility for first time
- Platform now used across 5 trials (expanded beyond initial 3)
Data Quality Improvements
- Automated reconciliation caught 127 cross-system discrepancies in first 3 months
- Query resolution time reduced by 35% through faster issue identification
- Data quality KRI scores improved by 18% over 6 months
- Platform validated and audit-ready for regulatory inspections
Key Takeaways
1. Custom Solutions Can Be More Cost-Effective
When commercial platforms require extensive customization anyway, a purpose-built solution can be faster and cheaper. This client saved $265K and got exactly the features they needed.
2. Data Integration Is the Hard Part
60% of the project timeline was spent on data integration and reconciliation logic. The dashboards themselves were straightforward once the data pipeline was solid.
3. R Shiny Is Production-Ready for RBQM
With proper architecture and hosting, R Shiny can support enterprise-scale RBQM programs. This platform now serves 50+ users across 5 trials with zero downtime.
Download Case Study
Get a one-page summary of this project for offline review or internal sharing.
Perfect for budget approval meetings and stakeholder reviews
Need a Custom RBQM Solution for Your Trials?
I design and build custom RBQM analytics platforms tailored to your specific data ecosystem. Whether you need EDC integration, ePRO reconciliation, or portfolio-level dashboards, I can deliver a production-ready solution.
Need Custom RBQM Solutions Built?
I design and build custom RBQM analytics platforms tailored to your specific data ecosystem. Whether you need EDC integration, ePRO reconciliation, or portfolio-level dashboards, I can deliver a production-ready solution. Let's discuss your project.